Publications
- G. I. Austin, I. Pe’er, and T. Korem, ‘Distributional bias compromises leave-one-out cross-validation’, Science Advances, 2025.
- G. I. Austin and T. Korem, ‘Compositional transformations can reasonably introduce phenotype-associated values into sparse features’, mSystems, 2025.
- G. I. Austin, A. Brown Kav, H. Park, J. Biermann, A.-C. Uhlemann, and T. Korem, ‘Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models’, Nature Microbiology, 2025.
- A. Batuure, G. I. Austin, W.A. Grobman et al., ‘A Simple Data-Driven Dietary Pattern Associated with Lower Risk for Adverse Pregnancy Outcomes’. American Journal of Obstetrics & Gynecology, 2025.
- G. I. Austin and T. Korem, ‘Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective’, The Journal of Infectious Diseases, p. jiae378, 2024.
- G. D. Sepich-Poore, D. McDonald, E. Kopylova, C. Guccione, Q. Zhu, G. Austin et al., ‘Robustness of cancer microbiome signals over a broad range of methodological variation’, Oncogene, vol. 43, no. 15, pp. 1127–1148, 2024.
- Q. S. Solfisburg, F. Baldini, B. Baldwin-Hunter, G. I. Austin et al., ‘The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett’s Esophagus and High-Grade Dysplasia or Adenocarcinoma’, Cancer Epidemiology, Biomarkers & Prevention, vol. 33, no. 3, pp. 371–380, 2024.
- H. Kowalkowski, G. Austin, Y. Guo, L.-A. Miller-Wilson, and S. D. Byfield, ‘Patterns of colorectal cancer screening and adherence rates among an average-risk population enrolled in a national health insurance provider during 2009-2018 in the United States’, Preventive Medicine Reports, vol. 36, p. 102497, 2023.
- G. I. Austin et al., ‘Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data’, Nature Biotechnology, vol. 41, no. 12, pp. 1820–1828, 2023.
- G. Austin et al., ‘Patterns of initial colorectal cancer screenings after turning 50 years old and follow-up rates of colonoscopy after positive stool-based testing among the average-risk population’, Current Medical Research and Opinion, vol. 39, no. 1, pp. 47–61, 2023.
Preprints
- W. F. Kindschuh*, G. I. Austin*, Y. Meydan, et al., ‘Early prediction of preeclampsia using the first trimester vaginal microbiome’, bioRxiv, 2024.
* Equal contribution
Patent submissions
- E. Halperin, B. Hill, and G. Austin, ‘Methods, apparatuses and computer program products for generating predicted multi-drug contraindication data objects’. US Patent App. 18/052,508 2024.
- G. Austin, E. Halperin, F. Mohaghegh, and A. C. Palomera, ‘Machine learning training approach for a multitask predictive domain’, US Patent App. 18/155,228 2024.
- G. Austin, J. Venkataraman, F. Mohaghegh, H.R. Hassanzadeh, J.D. Stremmel, A. Saeedi, G.D. Lyng, E. Halperin, and Z.M. Poornaki, ‘Systems and methods for intelligent model training using relevant data objects’, US Patent App. 18/428,206, 2025